refactor: divided the files in domain/model into separate files.

domain/model/column_inference.go

@@ -0,0 +1,182 @@
+// Package model provides domain model for filesql
+package model
+
+import (
+	"regexp"
+	"strconv"
+	"strings"
+	"time"
+)
+
+// Common datetime patterns to detect
+var datetimePatterns = []struct {
+	pattern *regexp.Regexp
+	formats []string // Multiple formats for the same pattern
+}{
+	// ISO8601 formats with timezone
+	{
+		regexp.MustCompile(`^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}(\.\d+)?(Z|[+-]\d{2}:\d{2})$`),
+		[]string{time.RFC3339, time.RFC3339Nano},
+	},
+	// ISO8601 formats without timezone
+	{
+		regexp.MustCompile(`^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}(\.\d+)?$`),
+		[]string{"2006-01-02T15:04:05", "2006-01-02T15:04:05.000"},
+	},
+	// ISO8601 date and time with space
+	{
+		regexp.MustCompile(`^\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}(\.\d+)?$`),
+		[]string{"2006-01-02 15:04:05", "2006-01-02 15:04:05.000"},
+	},
+	// ISO8601 date only
+	{
+		regexp.MustCompile(`^\d{4}-\d{2}-\d{2}$`),
+		[]string{"2006-01-02"},
+	},
+	// US formats
+	{
+		regexp.MustCompile(`^\d{1,2}/\d{1,2}/\d{4} \d{1,2}:\d{2}:\d{2}( (AM|PM))?$`),
+		[]string{"1/2/2006 15:04:05", "1/2/2006 3:04:05 PM", "01/02/2006 15:04:05"},
+	},
+	{
+		regexp.MustCompile(`^\d{1,2}/\d{1,2}/\d{4}$`),
+		[]string{"1/2/2006", "01/02/2006"},
+	},
+	// European formats
+	{
+		regexp.MustCompile(`^\d{1,2}\.\d{1,2}\.\d{4} \d{1,2}:\d{2}:\d{2}$`),
+		[]string{"2.1.2006 15:04:05", "02.01.2006 15:04:05"},
+	},
+	{
+		regexp.MustCompile(`^\d{1,2}\.\d{1,2}\.\d{4}$`),
+		[]string{"2.1.2006", "02.01.2006"},
+	},
+	// Time only
+	{
+		regexp.MustCompile(`^\d{1,2}:\d{2}:\d{2}(\.\d+)?$`),
+		[]string{"15:04:05", "15:04:05.000", "3:04:05"},
+	},
+	{
+		regexp.MustCompile(`^\d{1,2}:\d{2}$`),
+		[]string{"15:04", "3:04"},
+	},
+}
+
+// isDatetime checks if a string value represents a datetime
+func isDatetime(value string) bool {
+	value = strings.TrimSpace(value)
+	if value == "" {
+		return false
+	}
+
+	for _, dp := range datetimePatterns {
+		if dp.pattern.MatchString(value) {
+			// Try each format for this pattern
+			for _, format := range dp.formats {
+				if _, err := time.Parse(format, value); err == nil {
+					return true
+				}
+			}
+		}
+	}
+
+	return false
+}
+
+// InferColumnType infers the SQL column type from a slice of string values
+func InferColumnType(values []string) ColumnType {
+	if len(values) == 0 {
+		return ColumnTypeText
+	}
+
+	hasDatetime := false
+	hasReal := false
+	hasInteger := false
+	hasText := false
+
+	for _, value := range values {
+		// Skip empty values for type inference
+		value = strings.TrimSpace(value)
+		if value == "" {
+			continue
+		}
+
+		// Check if it's a datetime first (before checking numbers)
+		if isDatetime(value) {
+			hasDatetime = true
+			continue
+		}
+
+		// Try to parse as integer
+		if _, err := strconv.ParseInt(value, 10, 64); err == nil {
+			hasInteger = true
+			continue
+		}
+
+		// Try to parse as float
+		if _, err := strconv.ParseFloat(value, 64); err == nil {
+			hasReal = true
+			continue
+		}
+
+		// If it's not a number or datetime, it's text
+		hasText = true
+		break // If any value is text, the whole column is text
+	}
+
+	// Determine the most appropriate type
+	// Priority: TEXT > DATETIME > REAL > INTEGER
+	if hasText {
+		return ColumnTypeText
+	}
+	if hasDatetime {
+		return ColumnTypeDatetime
+	}
+	if hasReal {
+		return ColumnTypeReal
+	}
+	if hasInteger {
+		return ColumnTypeInteger
+	}
+
+	// Default to TEXT if no values were found
+	return ColumnTypeText
+}
+
+// InferColumnsInfo infers column information from header and data records
+func InferColumnsInfo(header Header, records []Record) []ColumnInfo {
+	columnCount := len(header)
+	if columnCount == 0 {
+		return nil
+	}
+
+	columns := make([]ColumnInfo, columnCount)
+
+	// Initialize column info with headers
+	for i, name := range header {
+		columns[i] = ColumnInfo{
+			Name: name,
+			Type: ColumnTypeText, // Default to TEXT
+		}
+	}
+
+	// If no records, return with TEXT types
+	if len(records) == 0 {
+		return columns
+	}
+
+	// Collect values for each column
+	for i := range columnCount {
+		var values []string
+		for _, record := range records {
+			if i < len(record) {
+				values = append(values, record[i])
+			}
+		}
+
+		// Infer type from values
+		columns[i].Type = InferColumnType(values)
+	}
+
+	return columns
+}